Deep Learning with Yacine on MSN

Build k-nearest neighbors from scratch in Python – step by step tutorial

Learn how to implement the K-Nearest Neighbors (KNN) algorithm from scratch in Python! This tutorial covers the theory, ...
Morning Overview on MSN

Scientists build a ‘periodic table’ for AI models

Scientists are trying to tame the chaos of modern artificial intelligence by doing something very old fashioned: drawing a ...
Visual Studio Magazine

Anomaly Detection Using K-Means Clustering with JavaScript

Dr. James McCaffrey presents a complete end-to-end demonstration of anomaly detection using k-means data clustering, implemented with JavaScript. Compared to other anomaly detection techniques, ...
IEEE

Improved K-Means Algorithm for Nearby Target Localization

Abstract: In a multi-source localization system, direction of arrival (DOA) estimation of angles always suffers from errors due to noise interference, sensor position inaccuracies, and other factors.
Frontiers

SMCFO: a novel cuttlefish optimization algorithm enhanced by simplex method for data clustering

Introduction: In unsupervised learning, data clustering is essential. However, many current algorithms have issues like early convergence, inadequate local search capabilities, and trouble processing ...
Frontiers

Automatic detection and classification of igneous rock fractures from imaging logging using K-mean algorithm and DNN

Accurately identifying fracture zones and their types in strata is of great significance for enhancing oil and gas recovery efficiency. Due to its complicated geological structure and long-term ...
IEEE

An Optimized Algorithm For Efficient Problem Solving In K-MEANS Clustering

Abstract: K-means clustering is used to cluster numerical data. In K-means we define two measures of distances, between two data points (records) and the distance between two clusters. Distance can be ...